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Abstract Rorqual foraging behavior varies with species, prey type and foraging conditions, and can be a determining factor for their fitness. Little is known about the foraging ecology of Rice’s whales (Balaenoptera ricei), an endangered species with a population of fewer than 100 individuals. Suction cup tags were attached to two Rice’s whales to collect information on their diving kinematics and foraging behavior. The tagged whales primarily exhibited lunge-feeding near the sea bottom and to a lesser extent in the water-column and at the sea surface. During 6–10 min foraging dives, the whales typically circled their prey before executing one or two feeding lunges. Longer duration dives and dives with more feeding-lunges were followed by an increase in their breathing rate. The median lunge rate of one lunge per dive of both animals was much lower than expected based on comparative research on other lunge-feeding baleen whales, and may be associated with foraging on fish instead of krill or may be an indication of different foraging conditions. Both animals spent extended periods of the night near the sea surface, increasing the risk for ship strike. Furthermore, their circling before lunging may increase the risk for entanglement in bottom-longline fishing gear. Overall, these data show that Rice’s whale foraging behavior differs from other lunge feeding rorqual species and may be a significant factor in shaping our understanding of their foraging ecology. Efforts to mitigate threats to Rice’s whales will benefit from improved understanding of patterns in their habitat use and fine-scale ecology.

Abstract Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board surveys provide high spatial resolution but lack temporal resolution and seasonal coverage. Stationary passive acoustic monitoring (PAM) employs acoustic sensors to sample point locations nearly continuously, providing high temporal resolution in local habitats across days, seasons and years. To evaluate whether cross-platform data synthesis can improve distribution predictions, models were developed for Cuvier’s beaked whales, sperm whales, and Risso’s dolphins in the oceanic Gulf of Mexico using two different methods: generalized additive models and neural networks. Neural networks were able to learn unspecified interactions between drivers. Models that incorporated PAM datasets out-performed models trained on visual data alone, and joint models performed best in two out of three cases. The modeling results suggest that, when taken together, multiple species distribution models using a variety of data types may support conservation and management of Gulf of Mexico cetacean populations by improving the understanding of temporal and spatial species distribution trends.